Chapter 6 Diversity analysis

6.1 Alpha diversity

# Calculate Hill numbers
richness <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 0) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(richness = 1) %>%
  rownames_to_column(var = "sample")

neutral <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(neutral = 1) %>%
  rownames_to_column(var = "sample")

phylogenetic <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1, tree = genome_tree) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(phylogenetic = 1) %>%
  rownames_to_column(var = "sample")

# Aggregate basal GIFT into elements
dist <- genome_gifts %>%
  to.elements(., GIFT_db3) %>%
  traits2dist(., method = "gower")

functional <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1, dist = dist) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(functional = 1) %>%
  rownames_to_column(var = "sample") %>%
  mutate(functional = if_else(is.nan(functional), 1, functional))

# Merge all metrics
alpha_div <- richness %>%
  full_join(neutral, by = join_by(sample == sample)) %>%
  full_join(phylogenetic, by = join_by(sample == sample)) %>%
  full_join(functional, by = join_by(sample == sample))

6.1.1 Wild samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#008080', "#d57d2c")) +
      scale_fill_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#00808050', "#d57d2c50")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.58) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.2 Acclimation samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="1_Acclimation") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#008080', "#d57d2c")) +
      scale_fill_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#00808050', "#d57d2c50")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.58) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.3 Antibiotics samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="2_Antibiotics") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#008080', "#d57d2c")) +
      scale_fill_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#00808050', "#d57d2c50")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.58) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.4 Transplant_1 samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="3_Transplant1") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.5 Transplant_2 samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="4_Transplant2") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.6 Post-Transplant_1 samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="5_Post-FMT1") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.7 Post-Transplant_2 samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-control","Warm-control", "Cold-intervention"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-control","Warm-control", "Cold-intervention"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.2 Beta diversity

beta_q0n <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 0)

beta_q1n <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1)

beta_q1p <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1, tree = genome_tree)

beta_q1f <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1, dist = dist)

6.3 Permanovas

6.3.0.1 Load required data

meta <- column_to_rownames(sample_metadata, "Tube_code")

6.3.1 1. Are the wild populations similar?

6.3.1.1 Wild: P.muralis vs P.liolepis

wild <- meta %>%
  filter(time_point == "0_Wild")

# Create a temporary modified version of genome_counts_filt
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

wild.counts <- temp_genome_counts[, which(colnames(temp_genome_counts) %in% rownames(wild))]
identical(sort(colnames(wild.counts)), sort(as.character(rownames(wild))))

wild_nmds <- sample_metadata %>%
  filter(time_point == "0_Wild")
6.3.1.1.1 Number of samples used
[1] 27
beta_div_richness_wild<-hillpair(data=wild.counts, q=0)
beta_div_neutral_wild<-hillpair(data=wild.counts, q=1)
beta_div_phylo_wild<-hillpair(data=wild.counts, q=1, tree=genome_tree)
beta_div_func_wild<-hillpair(data=wild.counts, q=1, dist=dist)

6.3.1.2 Richness

betadisper(beta_div_richness_wild$S, wild$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.000012 0.000012 0.0012    999  0.979
Residuals 25 0.257281 0.010291                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.981
Hot_dry   0.97302        
adonis2(formula=beta_div_richness_wild$S ~ Population, data=wild[labels(beta_div_richness_wild$S),], permutations=999) %>%
            as.matrix() %>%
            kable()
Df SumOfSqs R2 F Pr(>F)
Model 1 1.542719 0.2095041 6.625717 0.001
Residual 25 5.820951 0.7904959 NA NA
Total 26 7.363669 1.0000000 NA NA

6.3.1.3 Neutral

betadisper(beta_div_neutral_wild$S, wild$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     1 0.000048 0.0000476 0.0044    999  0.945
Residuals 25 0.270114 0.0108046                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.946
Hot_dry   0.94763        
adonis2(formula=beta_div_neutral_wild$S ~ Population, data=wild[labels(beta_div_neutral_wild$S),], permutations=999) %>%
            as.matrix() %>%
            kable()
Df SumOfSqs R2 F Pr(>F)
Model 1 1.918266 0.2608511 8.822682 0.001
Residual 25 5.435610 0.7391489 NA NA
Total 26 7.353876 1.0000000 NA NA

6.3.1.4 Phylogenetic

betadisper(beta_div_phylo_wild$S, wild$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.03585 0.035847 2.4912    999  0.108
Residuals 25 0.35973 0.014389                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.125
Hot_dry   0.12705        
adonis2(formula=beta_div_phylo_wild$S ~ Population, data=wild[labels(beta_div_phylo_wild$S),], permutations=999) %>%
            as.matrix() %>%
            kable()
Df SumOfSqs R2 F Pr(>F)
Model 1 0.3218613 0.2162815 6.899207 0.001
Residual 25 1.1662981 0.7837185 NA NA
Total 26 1.4881594 1.0000000 NA NA

6.3.1.5 Functional

betadisper(beta_div_func_wild$S, wild$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq  Mean Sq     F N.Perm Pr(>F)
Groups     1 0.019387 0.019387 1.653    999  0.216
Residuals 25 0.293200 0.011728                    

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.221
Hot_dry   0.21033        
adonis2(formula=beta_div_func_wild$S ~ Population, data=wild[labels(beta_div_func_wild$S),], permutations=999) %>%
            as.matrix() %>%
            kable()
Df SumOfSqs R2 F Pr(>F)
Model 1 0.0831048 0.1680538 5.05002 0.036
Residual 25 0.4114083 0.8319462 NA NA
Total 26 0.4945131 1.0000000 NA NA
beta_q0n_nmds_wild <- beta_div_richness_wild$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
                vegan::scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(wild_nmds, by = join_by(sample == Tube_code))

beta_q1n_nmds_wild <- beta_div_neutral_wild$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
                vegan::scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(wild_nmds, by = join_by(sample == Tube_code))

beta_q1p_nmds_wild <- beta_div_phylo_wild$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                vegan::scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(wild_nmds, by = join_by(sample == Tube_code))

beta_q1f_nmds_wild <- beta_div_func_wild$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                vegan::scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(wild_nmds, by = join_by(sample == Tube_code))

6.3.2 2. Effect of acclimation

accli <- meta %>%
  filter(time_point == "1_Acclimation")

# Create a temporary modified version of genome_counts_filt
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

accli.counts <- temp_genome_counts[, which(colnames(temp_genome_counts) %in% rownames(accli))]
identical(sort(colnames(accli.counts)), sort(as.character(rownames(accli))))

accli_nmds <- sample_metadata %>%
  filter(time_point == "1_Acclimation")
6.3.2.0.1 Number of samples used
[1] 27
beta_div_richness_accli<-hillpair(data=accli.counts, q=0)
beta_div_neutral_accli<-hillpair(data=accli.counts, q=1)
beta_div_phylo_accli<-hillpair(data=accli.counts, q=1, tree=genome_tree)
beta_div_func_accli<-hillpair(data=accli.counts, q=1, dist=dist)

6.3.2.1 Richness

betadisper(beta_div_richness_accli$S, accli$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)   
Groups     1 0.11796 0.117959 12.963    999  0.002 **
Residuals 25 0.22748 0.009099                        
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
          Cold_wet Hot_dry
Cold_wet             0.004
Hot_dry  0.0013711        
adonis2(formula=beta_div_richness_accli$S ~ Population, data=accli[labels(beta_div_richness_accli$S),], permutations=999) %>%
  as.matrix() %>%
  kable()
Df SumOfSqs R2 F Pr(>F)
Model 1 1.639807 0.179834 5.481634 0.001
Residual 25 7.478640 0.820166 NA NA
Total 26 9.118447 1.000000 NA NA

6.3.2.2 Neutral

betadisper(beta_div_neutral_accli$S, accli$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups     1 0.07844 0.078443 5.2384    999  0.026 *
Residuals 25 0.37437 0.014975                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.026
Hot_dry  0.030815        
adonis2(formula=beta_div_neutral_accli$S ~ Population, data=accli[labels(beta_div_neutral_accli$S),], permutations=999) %>%
  as.matrix() %>%
  kable()
Df SumOfSqs R2 F Pr(>F)
Model 1 1.947003 0.2306127 7.493387 0.001
Residual 25 6.495736 0.7693873 NA NA
Total 26 8.442739 1.0000000 NA NA

6.3.2.3 Phylogenetic

betadisper(beta_div_phylo_accli$S, accli$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.06739 0.067395 2.9532    999  0.105
Residuals 25 0.57052 0.022821                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.103
Hot_dry  0.098068        
adonis2(formula=beta_div_phylo_accli$S ~ Population, data=accli[labels(beta_div_phylo_accli$S),], permutations=999) %>%
  as.matrix() %>%
  kable()
Df SumOfSqs R2 F Pr(>F)
Model 1 0.2441653 0.1224638 3.488854 0.014
Residual 25 1.7496100 0.8775362 NA NA
Total 26 1.9937754 1.0000000 NA NA

6.3.2.4 Functional

betadisper(beta_div_func_accli$S, accli$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq     F N.Perm Pr(>F)
Groups     1 0.02351 0.023513 0.635    999  0.457
Residuals 25 0.92569 0.037028                    

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.446
Hot_dry   0.43303        
adonis2(formula=beta_div_func_accli$S ~ Population, data=accli[labels(beta_div_func_accli$S),], permutations=999) %>%
  as.matrix() %>%
  kable()
Df SumOfSqs R2 F Pr(>F)
Model 1 0.0279416 0.024809 0.6360037 0.452
Residual 25 1.0983283 0.975191 NA NA
Total 26 1.1262699 1.000000 NA NA
beta_q0n_nmds_accli <- beta_div_richness_accli$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(accli_nmds, by = join_by(sample == Tube_code))

beta_q1n_nmds_accli <- beta_div_neutral_accli$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(accli_nmds, by = join_by(sample == Tube_code))

beta_q1p_nmds_accli <- beta_div_phylo_accli$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(accli_nmds, by = join_by(sample == Tube_code))

beta_q1f_nmds_accli <- beta_div_func_accli$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(accli_nmds, by = join_by(sample == Tube_code))

6.3.3 3. Comparison between Wild and Acclimation

accli1 <- meta  %>%
  filter(time_point == "0_Wild" | time_point == "1_Acclimation")

temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

accli1.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(accli1))]
identical(sort(colnames(accli1.counts)),sort(as.character(rownames(accli1))))

accli1_nmds <- sample_metadata %>%
  filter(time_point == "0_Wild" | time_point == "1_Acclimation")
6.3.3.0.1 Number of samples used
[1] 54
beta_div_richness_accli1<-hillpair(data=accli1.counts, q=0)
beta_div_neutral_accli1<-hillpair(data=accli1.counts, q=1)
beta_div_phylo_accli1<-hillpair(data=accli1.counts, q=1, tree=genome_tree)
beta_div_func_accli1<-hillpair(data=accli1.counts, q=1, dist=dist)
#Arrange of metadata dataframe
accli1_arrange<-accli1[labels(beta_div_neutral_accli1$S),]
accli1_arrange$Population_time <- interaction(accli1_arrange$Population, accli1_arrange$time_point)
6.3.3.0.2 Richness
betadisper(beta_div_richness_accli1$S, accli1$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups     1 0.05014 0.050145 6.2252    999  0.015 *
Residuals 52 0.41886 0.008055                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
                0_Wild 1_Acclimation
0_Wild                         0.021
1_Acclimation 0.015808              
adonis2(formula=beta_div_richness_accli1$S ~ time_point*Population, data=accli1[labels(beta_div_richness_accli1$S),], permutations=999, strata=accli1$individual) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 3 3.799791 0.222218 4.761789 0.002
Residual 50 13.299591 0.777782 NA NA
Total 53 17.099381 1.000000 NA NA
pairwise<-pairwise.adonis(beta_div_richness_accli1$S,accli1_arrange$Population_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation 1 1.6398067 5.481634 0.17983399 0.001 0.006 *
Cold_wet.1_Acclimation vs Cold_wet.0_Wild 1 0.5861897 2.045106 0.05673741 0.003 0.018 .
Cold_wet.1_Acclimation vs Hot_dry.0_Wild 1 1.5524421 4.967864 0.16577304 0.001 0.006 *
Hot_dry.1_Acclimation vs Cold_wet.0_Wild 1 1.8259388 8.319131 0.24968031 0.001 0.006 *
Hot_dry.1_Acclimation vs Hot_dry.0_Wild 1 0.3856333 1.736034 0.09788177 0.007 0.042 .
Cold_wet.0_Wild vs Hot_dry.0_Wild 1 1.5427188 6.625717 0.20950408 0.001 0.006 *
6.3.3.0.3 Neutral
betadisper(beta_div_neutral_accli1$S, accli1$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     1 0.0199 0.0199035 2.1213    999  0.155
Residuals 52 0.4879 0.0093827                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
               0_Wild 1_Acclimation
0_Wild                        0.164
1_Acclimation 0.15128              
adonis2(formula=beta_div_neutral_accli1$S ~ time_point*Population, data=accli1[labels(beta_div_neutral_accli1$S),], permutations=999, strata=accli1$individual) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 3 4.770321 0.2856195 6.663569 0.001
Residual 50 11.931346 0.7143805 NA NA
Total 53 16.701667 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_neutral_accli1$S,accli1_arrange$Population_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation 1 1.9470028 7.493387 0.23061269 0.001 0.006 *
Cold_wet.1_Acclimation vs Cold_wet.0_Wild 1 0.8152517 3.211984 0.08631584 0.001 0.006 *
Cold_wet.1_Acclimation vs Hot_dry.0_Wild 1 2.1302445 7.742971 0.23647735 0.001 0.006 *
Hot_dry.1_Acclimation vs Cold_wet.0_Wild 1 2.0371666 10.078295 0.28730857 0.001 0.006 *
Hot_dry.1_Acclimation vs Hot_dry.0_Wild 1 0.6314393 3.060027 0.16054681 0.001 0.006 *
Cold_wet.0_Wild vs Hot_dry.0_Wild 1 1.9182663 8.822682 0.26085105 0.001 0.006 *
6.3.3.0.4 Phylogenetic
betadisper(beta_div_phylo_accli1$S, accli1$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.01334 0.013340 0.6524    999    0.4
Residuals 52 1.06332 0.020449                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
               0_Wild 1_Acclimation
0_Wild                        0.393
1_Acclimation 0.42294              
adonis2(formula=beta_div_phylo_accli1$S ~ time_point*Population, data=accli1[labels(beta_div_phylo_accli1$S),], permutations=999, strata=accli1$individual) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 3 0.855070 0.2267502 4.887385 0.001
Residual 50 2.915908 0.7732498 NA NA
Total 53 3.770978 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_phylo_accli1$S,accli1_arrange$Population_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation 1 0.2441653 3.488854 0.1224638 0.025 0.150
Cold_wet.1_Acclimation vs Cold_wet.0_Wild 1 0.2949378 4.881474 0.1255475 0.001 0.006 *
Cold_wet.1_Acclimation vs Hot_dry.0_Wild 1 0.3992196 4.442501 0.1508874 0.006 0.036 .
Hot_dry.1_Acclimation vs Cold_wet.0_Wild 1 0.2121479 7.924059 0.2406769 0.001 0.006 *
Hot_dry.1_Acclimation vs Hot_dry.0_Wild 1 0.2092433 3.885515 0.1953942 0.001 0.006 *
Cold_wet.0_Wild vs Hot_dry.0_Wild 1 0.3218613 6.899207 0.2162815 0.001 0.006 *
6.3.3.0.5 Functional
betadisper(beta_div_func_accli1$S, accli1$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.01264 0.012640 0.4951    999  0.476
Residuals 52 1.32764 0.025532                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              0_Wild 1_Acclimation
0_Wild                       0.495
1_Acclimation 0.4848              
adonis2(formula=beta_div_func_accli1$S ~ time_point*Population, data=accli1[labels(beta_div_func_accli1$S),], permutations=999, strata=accli1$individual) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 3 0.1558147 0.0935514 1.720109 0.313
Residual 50 1.5097366 0.9064486 NA NA
Total 53 1.6655513 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_func_accli1$S,accli1_arrange$Population_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation 1 0.027941631 0.6360037 0.02480900 0.435 1.000
Cold_wet.1_Acclimation vs Cold_wet.0_Wild 1 0.018947902 0.4777313 0.01385623 0.494 1.000
Cold_wet.1_Acclimation vs Hot_dry.0_Wild 1 0.145480076 3.6909571 0.12864531 0.057 0.342
Hot_dry.1_Acclimation vs Cold_wet.0_Wild 1 0.002523515 0.1203153 0.00478956 0.611 1.000
Hot_dry.1_Acclimation vs Hot_dry.0_Wild 1 0.040973276 4.0663305 0.20264445 0.098 0.588
Cold_wet.0_Wild vs Hot_dry.0_Wild 1 0.083104797 5.0500195 0.16805378 0.054 0.324
beta_richness_nmds_accli1 <- beta_div_richness_accli1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(accli1_nmds, by = c("sample" = "Tube_code"))

beta_neutral_nmds_accli1 <- beta_div_neutral_accli1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(accli1_nmds, by = c("sample" = "Tube_code"))

beta_phylo_nmds_accli1 <- beta_div_phylo_accli1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(accli1_nmds, by = join_by(sample == Tube_code))

beta_func_nmds_accli1 <- beta_div_func_accli1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(accli1_nmds, by = join_by(sample == Tube_code))

6.3.4 4. Effect of FMT on microbiota community

6.3.4.1 Comparison between Acclimation vs Post-FMT1

post3 <- meta  %>%
  filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1")

temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

post3.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post3))]
identical(sort(colnames(post3.counts)),sort(as.character(rownames(post3))))

post3_nmds <- sample_metadata %>%
  filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1")
6.3.4.1.1 Number of samples used
[1] 53
beta_div_richness_post3<-hillpair(data=post3.counts, q=0)
beta_div_neutral_post3<-hillpair(data=post3.counts, q=1)
beta_div_phylo_post3<-hillpair(data=post3.counts, q=1, tree=genome_tree)
beta_div_func_post3<-hillpair(data=post3.counts, q=1, dist=dist)
#Arrange of metadata dataframe
post3_arrange<-post3[labels(beta_div_neutral_post3$S),]
post3_arrange$type_time <- interaction(post3_arrange$type, post3_arrange$time_point)
6.3.4.1.2 Richness
betadisper(beta_div_richness_post3$S, post3_arrange$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq  Mean Sq      F N.Perm Pr(>F)    
Groups     2 0.099607 0.049803 9.5441    999  0.001 ***
Residuals 50 0.260911 0.005218                         
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
               Control Hot_control Treatment
Control                 0.00200000     0.890
Hot_control 0.00102653                 0.002
Treatment   0.88832670  0.00010131          
adonis2(formula=beta_div_richness_post3$S ~ time_point*type, data=post3[labels(beta_div_neutral_post3$S),], permutations=999,strata=post3$individual) %>%
  as.matrix() %>%
  kable()
Df SumOfSqs R2 F Pr(>F)
Model 5 4.403362 0.2369995 2.919782 0.001
Residual 47 14.176268 0.7630005 NA NA
Total 52 18.579631 1.0000000 NA NA
pairwise <- pairwise.adonis(beta_div_richness_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.3620815 1.052109 0.06169963 0.361 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 1.2800877 4.605444 0.22350616 0.001 0.015 .
Control.1_Acclimation vs Control.5_Post-FMT1 1 0.6845657 1.998114 0.11101796 0.004 0.060
Control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.8437461 2.499232 0.14281954 0.001 0.015 .
Control.1_Acclimation vs Hot_control.5_Post-FMT1 1 1.1208022 3.568670 0.18236649 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 1.3606630 5.087152 0.24124415 0.001 0.015 .
Treatment.1_Acclimation vs Control.5_Post-FMT1 1 0.7216200 2.172734 0.11956009 0.001 0.015 .
Treatment.1_Acclimation vs Treatment.5_Post-FMT1 1 0.9551308 2.926054 0.16322910 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 1 1.2263345 4.039487 0.20157637 0.001 0.015 .
Hot_control.1_Acclimation vs Control.5_Post-FMT1 1 1.4319792 5.384836 0.25180628 0.001 0.015 .
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.8172413 3.194690 0.17558364 0.001 0.015 .
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.5796135 2.441615 0.13239702 0.001 0.015 .
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.5615418 1.729004 0.10335366 0.017 0.255
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.8438429 2.793772 0.14865413 0.001 0.015 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.3734921 1.268929 0.07799710 0.115 1.000
6.3.4.1.3 Neutral
betadisper(beta_div_neutral_post3$S, post3$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     1 0.00945 0.0094472 1.1428    999  0.295
Residuals 51 0.42161 0.0082669                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 5_Post-FMT1
1_Acclimation                     0.286
5_Post-FMT1          0.2901            
adonis2(formula=beta_div_neutral_post3$S ~ time_point*type, data=post3[labels(beta_div_neutral_post3$S),], permutations=999,strata=post3$individual) %>%
  as.matrix() %>%
  kable()
Df SumOfSqs R2 F Pr(>F)
Model 5 5.302354 0.3027004 4.080576 0.001
Residual 47 12.214484 0.6972996 NA NA
Total 52 17.516838 1.0000000 NA NA
pairwise <- pairwise.adonis(beta_div_neutral_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.2316020 0.7712905 0.04598874 0.698 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 1.4015347 5.7562378 0.26457873 0.001 0.015 .
Control.1_Acclimation vs Control.5_Post-FMT1 1 0.8332162 2.9081103 0.15380227 0.003 0.045 .
Control.1_Acclimation vs Treatment.5_Post-FMT1 1 1.1719595 4.0685514 0.21336447 0.002 0.030 .
Control.1_Acclimation vs Hot_control.5_Post-FMT1 1 1.4260875 5.2413171 0.24675104 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 1.6347704 6.8326887 0.29925029 0.001 0.015 .
Treatment.1_Acclimation vs Control.5_Post-FMT1 1 0.9517634 3.3715700 0.17404733 0.001 0.015 .
Treatment.1_Acclimation vs Treatment.5_Post-FMT1 1 1.3127773 4.6298256 0.23585668 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 1 1.6713369 6.2395460 0.28056085 0.001 0.015 .
Hot_control.1_Acclimation vs Control.5_Post-FMT1 1 1.5409781 6.8338056 0.29928456 0.001 0.015 .
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.9133614 4.0964534 0.21451383 0.001 0.015 .
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.6954835 3.2951234 0.17077493 0.001 0.015 .
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.6051778 2.2508491 0.13047758 0.022 0.330
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 1.0528902 4.1436369 0.20570451 0.001 0.015 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.4150076 1.6372683 0.09840968 0.046 0.690
6.3.4.1.4 Phylogenetic
betadisper(beta_div_phylo_post3$S, post3$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.05132 0.051320 2.6745    999  0.112
Residuals 51 0.97861 0.019189                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 5_Post-FMT1
1_Acclimation                     0.107
5_Post-FMT1         0.10812            
adonis2(formula=beta_div_phylo_post3$S ~ time_point*type, data=post3[labels(beta_div_phylo_post3$S),], permutations=999,strata=post3$individual) %>%
  as.matrix() %>%
  kable()
Df SumOfSqs R2 F Pr(>F)
Model 5 0.7935087 0.2278753 2.774199 0.004
Residual 47 2.6886978 0.7721247 NA NA
Total 52 3.4822065 1.0000000 NA NA
pairwise <- pairwise.adonis(beta_div_phylo_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.04186923 0.4391642 0.02671451 0.724 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 0.15609416 2.5546889 0.13768428 0.034 0.510
Control.1_Acclimation vs Control.5_Post-FMT1 1 0.19193367 2.9749922 0.15678490 0.017 0.255
Control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.14627288 1.7907381 0.10665035 0.154 1.000
Control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.25061348 3.6146185 0.18428187 0.008 0.120
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 0.23108846 4.0521838 0.20208192 0.002 0.030 .
Treatment.1_Acclimation vs Control.5_Post-FMT1 1 0.26358465 4.3608960 0.21417997 0.003 0.045 .
Treatment.1_Acclimation vs Treatment.5_Post-FMT1 1 0.25319427 3.2738422 0.17915456 0.039 0.585
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.39050120 5.9837393 0.27218933 0.002 0.030 .
Hot_control.1_Acclimation vs Control.5_Post-FMT1 1 0.14203376 5.4200212 0.25303529 0.001 0.015 .
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.09666753 2.3682173 0.13635351 0.014 0.210
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.09252600 2.9824958 0.15711821 0.007 0.105
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.01842535 0.4144162 0.02688498 0.778 1.000
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.05987967 1.7387847 0.09802164 0.119 1.000
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.03212966 0.6477782 0.04139746 0.686 1.000
6.3.4.1.5 Functional
betadisper(beta_div_func_post3$S, post3$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     1 0.00554 0.0055401 0.2063    999  0.668
Residuals 51 1.36938 0.0268505                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 5_Post-FMT1
1_Acclimation                     0.658
5_Post-FMT1         0.65159            
adonis2(formula=beta_div_func_post3$S ~ time_point*type, data=post3[labels(beta_div_func_post3$S),], permutations=999,strata=post3$individual) %>%
  as.matrix() %>%
  kable()
Df SumOfSqs R2 F Pr(>F)
Model 5 0.2935075 0.1510442 1.672425 0.046
Residual 47 1.6496826 0.8489558 NA NA
Total 52 1.9431901 1.0000000 NA NA
pairwise <- pairwise.adonis(beta_div_func_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.090583070 1.66462866 0.094235135 0.206 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 0.086181392 1.63467278 0.092696519 0.213 1.000
Control.1_Acclimation vs Control.5_Post-FMT1 1 0.028641941 0.50417680 0.030548437 0.528 1.000
Control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.234795406 4.03037749 0.211786524 0.037 0.555
Control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.134726259 2.20299547 0.121023788 0.164 1.000
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 0.001022590 0.05430389 0.003382513 0.645 1.000
Treatment.1_Acclimation vs Control.5_Post-FMT1 1 0.002157067 0.09411569 0.005847832 0.615 1.000
Treatment.1_Acclimation vs Treatment.5_Post-FMT1 1 0.056602363 2.56037069 0.145803909 0.179 1.000
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.009569124 0.35095521 0.021463896 0.503 1.000
Hot_control.1_Acclimation vs Control.5_Post-FMT1 1 -0.001745663 -0.08225018 -0.005167199 0.746 1.000
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.057758674 2.84545622 0.159449901 0.159 1.000
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.005575266 0.21803560 0.013444020 0.542 1.000
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.119540855 4.84764704 0.244242909 0.058 0.870
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.052587837 1.77308932 0.099762584 0.226 1.000
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.012980354 0.44307662 0.028690955 0.479 1.000
beta_richness_nmds_post3 <- beta_div_richness_post3$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post3_nmds, by = c("sample" = "Tube_code"))

beta_neutral_nmds_post3 <- beta_div_neutral_post3$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post3_nmds, by = c("sample" = "Tube_code"))

beta_phylo_nmds_post3 <- beta_div_phylo_post3$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post3_nmds, by = join_by(sample == Tube_code))

beta_func_nmds_post3 <- beta_div_func_post3$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post3_nmds, by = join_by(sample == Tube_code))

6.3.4.2 Comparison between Acclimation vs Post-FMT2

post4 <- meta  %>%
  filter(time_point == "1_Acclimation" | time_point == "6_Post-FMT2")

temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

post4.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post4))]
identical(sort(colnames(post4.counts)),sort(as.character(rownames(post4))))

post4_nmds <- sample_metadata %>%
  filter(time_point == "1_Acclimation" | time_point == "6_Post-FMT2")
6.3.4.2.1 Number of samples used
[1] 54
beta_div_richness_post4<-hillpair(data=post4.counts, q=0)
beta_div_neutral_post4<-hillpair(data=post4.counts, q=1)
beta_div_phylo_post4<-hillpair(data=post4.counts, q=1, tree=genome_tree)
beta_div_func_post4<-hillpair(data=post4.counts, q=1, dist=dist)
#Arrange of metadata dataframe
post4_arrange<-post4[labels(beta_div_neutral_post4$S),]
post4_arrange$type_time <- interaction(post4_arrange$type, post4_arrange$time_point)
6.3.4.2.2 Richness
betadisper(beta_div_richness_post4$S, post4_arrange$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups     2 0.06809 0.034047 3.8471    999  0.027 *
Residuals 51 0.45135 0.008850                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              Control Hot_control Treatment
Control                 0.0430000     0.912
Hot_control 0.0349385                 0.005
Treatment   0.8855174   0.0047257          
adonis2(formula=beta_div_richness_post4$S ~ time_point*Population, data=post4[labels(beta_div_neutral_post4$S),], permutations=999,strata=post4$individual) %>%
  as.matrix() %>%
  kable()
Df SumOfSqs R2 F Pr(>F)
Model 3 3.310172 0.1883377 3.867324 0.001
Residual 50 14.265560 0.8116623 NA NA
Total 53 17.575732 1.0000000 NA NA
pairwise <- pairwise.adonis(beta_div_richness_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.3620815 1.052109 0.06169963 0.356 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 1.2800877 4.605444 0.22350616 0.001 0.015 .
Control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.8430295 2.845779 0.15100353 0.001 0.015 .
Control.1_Acclimation vs Control.6_Post-FMT2 1 0.5232174 1.683240 0.09518843 0.023 0.345
Control.1_Acclimation vs Hot_control.6_Post-FMT2 1 1.1217138 3.634271 0.18509835 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 1.3606630 5.087152 0.24124415 0.001 0.015 .
Treatment.1_Acclimation vs Treatment.6_Post-FMT2 1 0.9130048 3.195028 0.16645080 0.001 0.015 .
Treatment.1_Acclimation vs Control.6_Post-FMT2 1 0.5959230 1.984036 0.11032208 0.002 0.030 .
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 1 1.2747787 4.275366 0.21086503 0.001 0.015 .
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.6397330 2.913695 0.15405213 0.001 0.015 .
Hot_control.1_Acclimation vs Control.6_Post-FMT2 1 1.4575447 6.224524 0.28007456 0.001 0.015 .
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.3276169 1.412318 0.08111028 0.037 0.555
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 0.6463814 2.560441 0.13795154 0.001 0.015 .
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.4796256 1.916520 0.10696943 0.002 0.030 .
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 1.1305044 4.268317 0.21059061 0.001 0.015 .
6.3.4.2.3 Neutral
betadisper(beta_div_neutral_post4$S, post4_arrange$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     1 0.01544 0.0154447 2.0972    999  0.158
Residuals 52 0.38294 0.0073643                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 6_Post-FMT2
1_Acclimation                     0.161
6_Post-FMT2         0.15357            
adonis2(formula=beta_div_neutral_post4$S ~ time_point*Population, data=post4[labels(beta_div_neutral_post4$S),], permutations=999,strata=post4$individual) %>%
  as.matrix() %>%
  kable()
Df SumOfSqs R2 F Pr(>F)
Model 3 3.863228 0.229321 4.959284 0.001
Residual 50 12.983151 0.770679 NA NA
Total 53 16.846379 1.000000 NA NA
pairwise <- pairwise.adonis(beta_div_neutral_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.2316020 0.7712905 0.04598874 0.733 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 1.4015347 5.7562378 0.26457873 0.001 0.015 .
Control.1_Acclimation vs Treatment.6_Post-FMT2 1 1.1746426 4.5564741 0.22165640 0.001 0.015 .
Control.1_Acclimation vs Control.6_Post-FMT2 1 0.5286441 1.9819408 0.11021840 0.002 0.030 .
Control.1_Acclimation vs Hot_control.6_Post-FMT2 1 1.3443224 4.9104417 0.23483204 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 1.6347704 6.8326887 0.29925029 0.001 0.015 .
Treatment.1_Acclimation vs Treatment.6_Post-FMT2 1 1.3540292 5.3398081 0.25022756 0.001 0.015 .
Treatment.1_Acclimation vs Control.6_Post-FMT2 1 0.6311089 2.4041625 0.13063146 0.002 0.030 .
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 1 1.6125755 5.9825981 0.27215155 0.001 0.015 .
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.6202327 3.1519868 0.16457754 0.001 0.015 .
Hot_control.1_Acclimation vs Control.6_Post-FMT2 1 1.5701179 7.6327037 0.32297209 0.001 0.015 .
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.3634438 1.7083388 0.09647087 0.036 0.540
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 1.0227481 4.6483346 0.22511910 0.001 0.015 .
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.5010202 2.2065321 0.12119453 0.001 0.015 .
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 1.3619424 5.7710313 0.26507845 0.001 0.015 .
6.3.4.2.4 Phylogenetic
betadisper(beta_div_phylo_post4$S, post4$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups     1 0.06978 0.069777 5.0345    999  0.018 *
Residuals 52 0.72071 0.013860                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 6_Post-FMT2
1_Acclimation                     0.022
6_Post-FMT2        0.029131            
adonis2(formula=beta_div_phylo_post4$S ~ time_point*Population, data=post4[labels(beta_div_phylo_post4$S),], permutations=999,strata=post4$individual) %>%
  as.matrix() %>%
  kable()
Df SumOfSqs R2 F Pr(>F)
Model 3 0.757493 0.2376349 5.195124 0.001
Residual 50 2.430141 0.7623651 NA NA
Total 53 3.187634 1.0000000 NA NA
pairwise <- pairwise.adonis(beta_div_phylo_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.04186923 0.4391642 0.02671451 0.753 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 0.15609416 2.5546889 0.13768428 0.035 0.525
Control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.26322331 4.3060281 0.21205664 0.003 0.045 .
Control.1_Acclimation vs Control.6_Post-FMT2 1 0.16047895 2.5405742 0.13702781 0.037 0.555
Control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.25529510 4.0109138 0.20043631 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 0.23108846 4.0521838 0.20208192 0.002 0.030 .
Treatment.1_Acclimation vs Treatment.6_Post-FMT2 1 0.36496892 6.3966666 0.28560797 0.001 0.015 .
Treatment.1_Acclimation vs Control.6_Post-FMT2 1 0.22628210 3.8292220 0.19311005 0.018 0.270
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.34830814 5.8463335 0.26761166 0.001 0.015 .
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.10002871 4.3836237 0.21505615 0.002 0.030 .
Hot_control.1_Acclimation vs Control.6_Post-FMT2 1 0.12577510 5.0601287 0.24027055 0.001 0.015 .
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.06334378 2.4997737 0.13512455 0.019 0.285
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 0.05927454 2.3820253 0.12958449 0.023 0.345
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.06906280 2.7224602 0.14541146 0.003 0.045 .
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.11081709 4.0436561 0.20174244 0.001 0.015 .
6.3.4.2.5 Functional
betadisper(beta_div_func_post4$S, post4$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.00527 0.005269 0.1889    999  0.639
Residuals 52 1.45058 0.027896                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 6_Post-FMT2
1_Acclimation                     0.646
6_Post-FMT2         0.66565            
adonis2(formula=beta_div_func_post4$S ~ time_point*Population, data=post4[labels(beta_div_func_post4$S),], permutations=999,strata=post4$individual) %>%
  as.matrix() %>%
  kable()
Df SumOfSqs R2 F Pr(>F)
Model 3 0.0773959 0.0417692 0.726498 0.266
Residual 50 1.7755477 0.9582308 NA NA
Total 53 1.8529436 1.0000000 NA NA
pairwise <- pairwise.adonis(beta_div_func_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.0905830704 1.664628661 0.0942351347 0.227 1
Control.1_Acclimation vs Hot_control.1_Acclimation 1 0.0861813922 1.634672780 0.0926965190 0.212 1
Control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.1197900330 2.213130846 0.1215129274 0.170 1
Control.1_Acclimation vs Control.6_Post-FMT2 1 0.1125623700 2.150784454 0.1184953995 0.169 1
Control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.0657004998 0.954588109 0.0563026423 0.258 1
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 0.0010225901 0.054303886 0.0033825127 0.630 1
Treatment.1_Acclimation vs Treatment.6_Post-FMT2 1 -0.0005177706 -0.025585400 -0.0016016487 0.720 1
Treatment.1_Acclimation vs Control.6_Post-FMT2 1 0.0013301207 0.072110871 0.0044867082 0.617 1
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.0060959077 0.174487757 0.0107878382 0.579 1
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.0010345754 0.055797964 0.0034752533 0.685 1
Hot_control.1_Acclimation vs Control.6_Post-FMT2 1 -0.0001056284 -0.006306177 -0.0003942915 0.703 1
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.0017235602 0.051851181 0.0032302306 0.764 1
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 -0.0080428882 -0.442986255 -0.0284750185 0.840 1
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 -0.0011796256 -0.034047378 -0.0021324990 0.895 1
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.0036300838 0.110487573 0.0068581148 0.690 1
beta_richness_nmds_post4 <- beta_div_richness_post4$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post4_nmds, by = c("sample" = "Tube_code"))

beta_neutral_nmds_post4 <- beta_div_neutral_post4$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post4_nmds, by = c("sample" = "Tube_code"))

beta_phylo_nmds_post4 <- beta_div_phylo_post4$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post4_nmds, by = join_by(sample == Tube_code))

beta_func_nmds_post4 <- beta_div_func_post4$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post4_nmds, by = join_by(sample == Tube_code))

6.3.5 5. Are there differences between the control and the treatment group?

6.3.5.1 After 1 week –> Post-FMT1

post1 <- meta %>%
  filter(time_point == "5_Post-FMT1")

post1.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post1))]
identical(sort(colnames(post1.counts)),sort(as.character(rownames(post1))))

post1_nmds <- sample_metadata %>%
  filter(time_point == "5_Post-FMT1")
6.3.5.1.1 Number of samples used
[1] 26
beta_div_richness_post1<-hillpair(data=post1.counts, q=0)
beta_div_neutral_post1<-hillpair(data=post1.counts, q=1)
beta_div_phylo_post1<-hillpair(data=post1.counts, q=1, tree=genome_tree)
beta_div_func_post1<-hillpair(data=post1.counts, q=1, dist=dist)
#Arrange of metadata dataframe
post1_arrange<-post1[labels(beta_div_neutral_post1$S),]
6.3.5.1.2 Richness
betadisper(beta_div_richness_post1$S, post1$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.017675 0.0088373 2.3825    999  0.107
Residuals 23 0.085312 0.0037092                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              Control Hot_control Treatment
Control                 0.0100000     0.672
Hot_control 0.0068795                 0.213
Treatment   0.6248469   0.2084296          
adonis2(formula=beta_div_richness_post1$S ~ Population+type, data=post1[labels(beta_div_richness_post1$S),], permutations=999) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 2 1.195567 0.1448246 1.947534 0.001
Residual 23 7.059710 0.8551754 NA NA
Total 25 8.255277 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_richness_post1$S,post1_arrange$type, perm=999)
pairwise
                     pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
1     Control vs Treatment  1 0.5615418 1.729004 0.1033537   0.020      0.060    
2   Control vs Hot_control  1 0.8438429 2.793772 0.1486541   0.001      0.003   *
3 Treatment vs Hot_control  1 0.3734921 1.268929 0.0779971   0.099      0.297    
6.3.5.1.3 Neutral
betadisper(beta_div_neutral_post1$S, post1$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.011001 0.0055005 0.6303    999  0.565
Residuals 23 0.200714 0.0087267                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.20000     0.956
Hot_control 0.21166                 0.457
Treatment   0.95468     0.43604          
adonis2(formula=beta_div_neutral_post1$S ~ Population+type, data=post1[labels(beta_div_neutral_post1$S),], permutations=999) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 2 1.395968 0.1900228 2.697931 0.002
Residual 23 5.950350 0.8099772 NA NA
Total 25 7.346318 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_neutral_post1$S,post1_arrange$type, perm=999)
pairwise
                     pairs Df SumsOfSqs  F.Model         R2 p.value p.adjusted sig
1     Control vs Treatment  1 0.6051778 2.250849 0.13047758   0.021      0.063    
2   Control vs Hot_control  1 1.0528902 4.143637 0.20570451   0.001      0.003   *
3 Treatment vs Hot_control  1 0.4150076 1.637268 0.09840968   0.052      0.156    
6.3.5.1.4 Phylogenetic
betadisper(beta_div_phylo_post1$S, post1$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.00440 0.0021994 0.1369    999  0.916
Residuals 23 0.36941 0.0160614                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.92500     0.691
Hot_control 0.91505                 0.784
Treatment   0.63312     0.73046          
adonis2(formula=beta_div_phylo_post1$S ~ Population+type, data=post1[labels(beta_div_phylo_post1$S),], permutations=999) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 2 0.0745104 0.0705947 0.8735033 0.547
Residual 23 0.9809570 0.9294053 NA NA
Total 25 1.0554673 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_phylo_post1$S,post1_arrange$type, perm=999)
pairwise
                     pairs Df  SumsOfSqs   F.Model         R2 p.value p.adjusted sig
1     Control vs Treatment  1 0.01842535 0.4144162 0.02688498   0.767      1.000    
2   Control vs Hot_control  1 0.05987967 1.7387847 0.09802164   0.108      0.324    
3 Treatment vs Hot_control  1 0.03212966 0.6477782 0.04139746   0.710      1.000    
6.3.5.1.5 Functional
betadisper(beta_div_func_post1$S, post1$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.00400 0.0019999 0.1431    999  0.881
Residuals 23 0.32135 0.0139717                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.64100     0.737
Hot_control 0.60188                 0.857
Treatment   0.74597     0.84473          
adonis2(formula=beta_div_func_post1$S ~ Population+type, data=post1[labels(beta_div_func_post1$S),], permutations=999) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 2 0.1230554 0.1608583 2.204479 0.175
Residual 23 0.6419374 0.8391417 NA NA
Total 25 0.7649929 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_func_post1$S,post1_arrange$type, perm=999)
pairwise
                     pairs Df  SumsOfSqs   F.Model         R2 p.value p.adjusted sig
1     Control vs Treatment  1 0.11954085 4.8476470 0.24424291   0.058      0.174    
2   Control vs Hot_control  1 0.05258784 1.7730893 0.09976258   0.225      0.675    
3 Treatment vs Hot_control  1 0.01298035 0.4430766 0.02869096   0.468      1.000    
beta_richness_nmds_post1 <- beta_div_richness_post1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post1_nmds, by = join_by(sample == Tube_code))

beta_neutral_nmds_post1 <- beta_div_neutral_post1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post1_nmds, by = join_by(sample == Tube_code))

beta_phylogenetic_nmds_post1 <- beta_div_phylo_post1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post1_nmds, by = join_by(sample == Tube_code))

beta_functional_nmds_post1 <- beta_div_func_post1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post1_nmds, by = join_by(sample == Tube_code))
p0<-beta_richness_nmds_post1 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p1<-beta_neutral_nmds_post1 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
                theme_classic() +
                theme(legend.position="none")
  
p2<-beta_phylogenetic_nmds_post1 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p3<-beta_functional_nmds_post1 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
                theme_classic()+
                theme(legend.position="none")
ggarrange(p0, p1, p2, p3, ncol=2, nrow=2, common.legend = TRUE, legend="right")

6.3.5.2 After 2 weeks –>Post-FMT2

post2 <- meta %>%
  filter(time_point == "6_Post-FMT2")

post2.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post2))]
identical(sort(colnames(post2.counts)),sort(as.character(rownames(post2))))

post2_nmds <- sample_metadata %>%
  filter(time_point == "6_Post-FMT2")
6.3.5.2.1 Number of samples used
[1] 27
beta_div_richness_post2<-hillpair(data=post2.counts, q=0)
beta_div_neutral_post2<-hillpair(data=post2.counts, q=1)
beta_div_phylo_post2<-hillpair(data=post2.counts, q=1, tree=genome_tree)
beta_div_func_post2<-hillpair(data=post2.counts, q=1, dist=dist)
#Arrange of metadata dataframe
post2_arrange<-post2[labels(beta_div_neutral_post2$S),]
6.3.5.2.2 Richness
betadisper(beta_div_richness_post2$S, post2$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.002011 0.0010056 0.1982    999  0.829
Residuals 24 0.121775 0.0050740                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.69200     0.770
Hot_control 0.67789                 0.643
Treatment   0.79246     0.59820          
adonis2(formula=beta_div_richness_post2$S ~ type, data=post2[labels(beta_div_richness_post2$S),], permutations=999) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 2 1.504341 0.1967776 2.939822 0.001
Residual 24 6.140538 0.8032224 NA NA
Total 26 7.644879 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_richness_post2$S,post2_arrange$type, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Treatment vs Control 1 0.6463814 2.560441 0.1379515 0.001 0.003 *
Treatment vs Hot_control 1 0.4796256 1.916520 0.1069694 0.001 0.003 *
Control vs Hot_control 1 1.1305044 4.268317 0.2105906 0.001 0.003 *
6.3.5.2.3 Neutral
betadisper(beta_div_neutral_post2$S, post2$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.008262 0.0041311 0.8024    999  0.434
Residuals 24 0.123559 0.0051483                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.44100     0.661
Hot_control 0.44675                 0.230
Treatment   0.65989     0.25095          
adonis2(formula=beta_div_neutral_post2$S ~ type, data=post2[labels(beta_div_neutral_post2$S),], permutations=999) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 2 1.923807 0.2603795 4.224537 0.001
Residual 24 5.464666 0.7396205 NA NA
Total 26 7.388473 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_neutral_post2$S,post2_arrange$type, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Treatment vs Control 1 1.0227481 4.648335 0.2251191 0.001 0.003 *
Treatment vs Hot_control 1 0.5010202 2.206532 0.1211945 0.002 0.006 *
Control vs Hot_control 1 1.3619424 5.771031 0.2650785 0.002 0.006 *
6.3.5.2.4 Phylogenetic
betadisper(beta_div_phylo_post2$S, post2$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.000407 0.0002034 0.0487    999  0.942
Residuals 24 0.100305 0.0041794                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.93700     0.829
Hot_control 0.93765                 0.754
Treatment   0.83933     0.76015          
adonis2(formula=beta_div_phylo_post2$S ~ type, data=post2[labels(beta_div_phylo_post2$S),], permutations=999) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 2 0.1594363 0.2042241 3.079623 0.001
Residual 24 0.6212564 0.7957759 NA NA
Total 26 0.7806927 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_phylo_post2$S,post2_arrange$type, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Treatment vs Control 1 0.05927454 2.382025 0.1295845 0.027 0.081
Treatment vs Hot_control 1 0.06906280 2.722460 0.1454115 0.003 0.009 *
Control vs Hot_control 1 0.11081709 4.043656 0.2017424 0.001 0.003 *
6.3.5.2.5 Functional
betadisper(beta_div_func_post2$S, post2$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.01259 0.0062962 0.3249    999  0.746
Residuals 24 0.46507 0.0193778                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.51300     0.626
Hot_control 0.45381                 0.792
Treatment   0.57452     0.74365          
adonis2(formula=beta_div_func_post2$S ~ type, data=post2[labels(beta_div_func_post2$S),], permutations=999) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 2 -0.0037283 -0.0054704 -0.065288 0.924
Residual 24 0.6852623 1.0054704 NA NA
Total 26 0.6815340 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_func_post2$S,post2_arrange$type, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Treatment vs Control 1 -0.008042888 -0.44298625 -0.028475019 0.853 1
Treatment vs Hot_control 1 -0.001179626 -0.03404738 -0.002132499 0.888 1
Control vs Hot_control 1 0.003630084 0.11048757 0.006858115 0.692 1
beta_richness_nmds_post2 <- beta_div_richness_post2$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post2_nmds, by = join_by(sample == Tube_code))

beta_neutral_nmds_post2 <- beta_div_neutral_post2$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post2_nmds, by = join_by(sample == Tube_code))

beta_phylogenetic_nmds_post2 <- beta_div_phylo_post2$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post2_nmds, by = join_by(sample == Tube_code))

beta_functional_nmds_post2 <- beta_div_func_post2$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post2_nmds, by = join_by(sample == Tube_code))
p0<-beta_richness_nmds_post2 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p1<-beta_neutral_nmds_post2 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
                theme_classic() +
                theme(legend.position="none")
  
p2<-beta_phylogenetic_nmds_post2 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p3<-beta_functional_nmds_post2 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
                theme_classic()+
                theme(legend.position="none")
ggarrange(p0, p1, p2, p3, ncol=2, nrow=2, common.legend = TRUE, legend="right")

6.3.5.3 Post1 vs Post2

post5 <- meta %>%
  filter(time_point == "6_Post-FMT2" | time_point == "5_Post-FMT1")

post5.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post5))]
identical(sort(colnames(post5.counts)),sort(as.character(rownames(post5))))

post5_nmds <- sample_metadata %>%
  filter(time_point == "6_Post-FMT2"| time_point == "5_Post-FMT1")
6.3.5.3.1 Number of samples used
[1] 53
beta_div_richness_post5<-hillpair(data=post5.counts, q=0)
beta_div_neutral_post5<-hillpair(data=post5.counts, q=1)
beta_div_phylo_post5<-hillpair(data=post5.counts, q=1, tree=genome_tree)
beta_div_func_post5<-hillpair(data=post5.counts, q=1, dist=dist)
#Arrange of metadata dataframe
post5_arrange<-post5[labels(beta_div_neutral_post5$S),]
post5_arrange$type_time <- interaction(post5_arrange$type, post5_arrange$time_point)
6.3.5.3.2 Richness
betadisper(beta_div_richness_post5$S, post5$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.01841 0.0092048 1.7364    999  0.171
Residuals 50 0.26505 0.0053010                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
             Control Hot_control Treatment
Control                 0.027000     0.724
Hot_control 0.039117                 0.211
Treatment   0.716358    0.218648          
adonis2(formula=beta_div_richness_post5$S ~ type*time_point, data=post5[labels(beta_div_richness_post5$S),], permutations=999,strata=post5$individual) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 5 3.261916 0.1981463 2.322836 0.004
Residual 47 13.200248 0.8018537 NA NA
Total 52 16.462165 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_richness_post5$S,post5_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.5615418 1.729004 0.10335366 0.016 0.240
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.8438429 2.793772 0.14865413 0.001 0.015 .
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.7628135 2.683925 0.14364890 0.001 0.015 .
Control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.3432605 1.148733 0.06698647 0.272 1.000
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 1.1269580 3.799256 0.19188884 0.001 0.015 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.3734921 1.268929 0.07799710 0.107 1.000
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.3571397 1.297184 0.07959561 0.123 1.000
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.7769467 2.670898 0.15114670 0.001 0.015 .
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.6502360 2.253407 0.13060650 0.001 0.015 .
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.4132091 1.616138 0.09174188 0.003 0.045 .
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 1 1.0163992 3.760571 0.19030682 0.001 0.015 .
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.2732563 1.019281 0.05988979 0.433 1.000
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 0.6463814 2.560441 0.13795154 0.001 0.015 .
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.4796256 1.916520 0.10696943 0.001 0.015 .
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 1.1305044 4.268317 0.21059061 0.001 0.015 .
6.3.5.3.3 Neutral
betadisper(beta_div_neutral_post5$S, post5$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq     F N.Perm Pr(>F)
Groups     2 0.01992 0.0099587 1.565    999  0.219
Residuals 50 0.31818 0.0063636                    

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.11200     0.872
Hot_control 0.10701                 0.187
Treatment   0.87156     0.17449          
adonis2(formula=beta_div_neutral_post5$S ~ type*time_point, data=post5[labels(beta_div_neutral_post5$S),], permutations=999,strata=post5$individual) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 5 3.947979 0.2569798 3.251069 0.002
Residual 47 11.415016 0.7430202 NA NA
Total 52 15.362995 1.0000000 NA NA
pairwise<-pairwise.adonis(beta_div_neutral_post5$S,post5_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.6051778 2.250849 0.13047758 0.017 0.255
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 1.0528902 4.143637 0.20570451 0.001 0.015 .
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.8908158 3.714692 0.18842252 0.001 0.015 .
Control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.3860927 1.552176 0.08843210 0.102 1.000
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 1.3122237 5.130273 0.24279254 0.001 0.015 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.4150076 1.637268 0.09840968 0.057 0.855
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.3157079 1.325203 0.08117526 0.167 1.000
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 1 1.0579520 4.270010 0.22158835 0.001 0.015 .
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.7454015 2.920049 0.16294873 0.002 0.030 .
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.4377161 1.942126 0.10824392 0.005 0.075
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 1 1.3766597 5.875279 0.26858075 0.001 0.015 .
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.3176516 1.316137 0.07600637 0.157 1.000
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 1.0227481 4.648335 0.22511910 0.001 0.015 .
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.5010202 2.206532 0.12119453 0.001 0.015 .
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 1.3619424 5.771031 0.26507845 0.001 0.015 .
6.3.5.3.4 Phylogenetic
betadisper(beta_div_phylo_post5$S, post5$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.00051 0.0002543 0.0265    999  0.974
Residuals 50 0.47996 0.0095993                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.90600     0.838
Hot_control 0.88926                 0.922
Treatment   0.82391     0.91902          
adonis2(formula=beta_div_phylo_post5$S ~ type*time_point, data=post5[labels(beta_div_phylo_post5$S),], permutations=999,strata=post5$individual) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 5 0.3518222 0.180049 2.0641 0.042
Residual 47 1.6022134 0.819951 NA NA
Total 52 1.9540356 1.000000 NA NA
pairwise<-pairwise.adonis(beta_div_phylo_post5$S,post5_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.01842535 0.4144162 0.02688498 0.759 1.000
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.05987967 1.7387847 0.09802164 0.114 1.000
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.07917244 3.0180046 0.15869197 0.003 0.045 .
Control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.04335491 1.5335604 0.08746429 0.199 1.000
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.10783045 3.7500438 0.18987521 0.001 0.015 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.03212966 0.6477782 0.04139746 0.688 1.000
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.06393539 1.5651817 0.09448624 0.132 1.000
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.05265949 1.2240203 0.07544494 0.305 1.000
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.09753501 2.2402429 0.12994265 0.010 0.150
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.07228545 2.3279593 0.12701683 0.040 0.600
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.11759094 3.5538444 0.18174658 0.001 0.015 .
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.06667255 1.9859527 0.11041687 0.096 1.000
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 0.05927454 2.3820253 0.12958449 0.032 0.480
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.06906280 2.7224602 0.14541146 0.004 0.060
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.11081709 4.0436561 0.20174244 0.002 0.030 .
6.3.5.3.5 Functional
betadisper(beta_div_func_post5$S, post5$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.00785 0.0039232 0.2322    999  0.815
Residuals 50 0.84483 0.0168966                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.53000     0.609
Hot_control 0.52384                 0.861
Treatment   0.58787     0.85068          
adonis2(formula=beta_div_func_post5$S ~ type*time_point, data=post5[labels(beta_div_func_post5$S),], permutations=999,strata=post5$individual) %>%
        as.matrix() %>%
        kable()
Df SumOfSqs R2 F Pr(>F)
Model 5 0.1076682 0.075037 0.7625685 0.473
Residual 47 1.3271997 0.924963 NA NA
Total 52 1.4348679 1.000000 NA NA
pairwise<-pairwise.adonis(beta_div_func_post5$S,post5_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.1195408549 4.84764704 0.2442429086 0.075 1
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.0525878365 1.77308932 0.0997625840 0.226 1
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.0265995825 1.17541806 0.0684360667 0.311 1
Control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.0145818992 0.69975992 0.0419023938 0.408 1
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 -0.0080695208 -0.21617323 -0.0136958691 0.916 1
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.0129803540 0.44307662 0.0286909552 0.462 1
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.0267162134 1.22560581 0.0755352882 0.297 1
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.0384388433 1.93281582 0.1141461550 0.231 1
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.0553988290 1.47819391 0.0897060633 0.241 1
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 -0.0040061386 -0.14850469 -0.0093684974 0.718 1
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.0024023972 0.09538980 0.0059265296 0.617 1
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 -0.0004960759 -0.01190328 -0.0007445087 0.842 1
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 -0.0080428882 -0.44298625 -0.0284750185 0.822 1
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 -0.0011796256 -0.03404738 -0.0021324990 0.917 1
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.0036300838 0.11048757 0.0068581148 0.696 1
beta_richness_nmds_post5 <- beta_div_richness_post5$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post5_nmds, by = join_by(sample == Tube_code))

beta_neutral_nmds_post5 <- beta_div_neutral_post5$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post5_nmds, by = join_by(sample == Tube_code))

beta_phylogenetic_nmds_post5 <- beta_div_phylo_post5$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post5_nmds, by = join_by(sample == Tube_code))

beta_functional_nmds_post5 <- beta_div_func_post5$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(post5_nmds, by = join_by(sample == Tube_code))
p0<-beta_richness_nmds_post5 %>%
            group_by(type, time_point) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p1<-beta_neutral_nmds_post5 %>%
            group_by(type, time_point) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point, label=sample)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        geom_text()
        labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
                theme_classic() +
                theme(legend.position="none")
  
p2<-beta_phylogenetic_nmds_post5 %>%
            group_by(type, time_point) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p3<-beta_functional_nmds_post5 %>%
            group_by(type, time_point) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
                theme_classic()+
                theme(legend.position="none")
ggarrange(p0, p1, p2, p3, ncol=2, nrow=2, common.legend = TRUE, legend="right")